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Dr. Ka-fu Wong. ECON1003 Analysis of Economic Data. Chapter One. What is Statistics?. GOALS. Understand why we study statistics. Explain what is meant by descriptive statistics and inferential statistics. Distinguish between a qualitative variable and a quantitative variable. - PowerPoint PPT Presentation

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No Slide TitleExplain what is meant by descriptive statistics and inferential statistics.

Distinguish between a qualitative variable and a quantitative variable.

Distinguish between a discrete variable and a continuous variable.

Distinguish among the nominal, ordinal, interval, and ratio levels of measurement.

Define the terms mutually exclusive and exhaustive.

Chapter One

collecting,

organizing,

presenting,

Ka-fu Wong © 2003

Who Uses Statistics?

Economists,

marketing,

accounting,

We need to provide forecast of the economy (GDP growth).

We need quantitative estimates of

How individual decisions are influenced by policy variables (such as unemployment benefits, education subsidy) in order to forecast the impact of public policies.

How macro policies (government expenditure) will affect output.

Ka-fu Wong © 2003

Who Uses Statistics?

In the business community,

managers must make decisions based on what will happen to such things as

demand,

profits.

These decisions are an effort to shape the future of the organization.

If the managers make no effort to look at the past and extrapolate into the future, the likelihood of achieving success is slim.

Ka-fu Wong © 2003

Why do we need to understand Statistics?

We are constantly deluged with statistics in the media (newspapers, magazines, journals, text books, etc.).

We need to have a means to condense large quantities of information into a few facts or figures.

We need to predict what will likely occur given what has occurred in the past.

We need to generalize what we have learned in specific situations to the more general case.

Ka-fu Wong © 2003

We do not want to become professors of statistics.

We do not want to develop advanced statistics theory.

We are users of statistics

To be effective users, we need to have a good grip of basic statistics theory.

We need to practice using the tools.

This course will give you the basic, enough for you to move on to your next Econometrics class.

Ka-fu Wong © 2003

Types of Statistics

Examples:

A Gallup poll found that 49% of the people in a survey knew the name of the first book of the Bible. The statistic 49 describes the number out of every 100 persons who knew the answer.

According to Consumer Reports, General Electric washing machine owners reported 9 problems per 100 machines during 2001. The statistic 9 describes the number of problems out of every 100 machines.

In our class, xx are female out of yy.

On 2 January, xx% of stocks listed in HK closed lower than the previous trading day.

Ka-fu Wong © 2003

Types of Statistics

Inferential Statistics: A decision, estimate, prediction, or generalization about a population, based on a sample.

A population is a collection of all possible individuals, objects, or measurements of interest.

A sample is a portion, or part, of the population of interest.

Ka-fu Wong © 2003

Types of Statistics

(examples of inferential statistics)

TV networks constantly monitor the popularity of their programs by hiring Nielsen and other organizations to sample the preferences of TV viewers.

The accounting department of a large firm will select a sample of the invoices to check for accuracy for all the invoices of the company.

Wine tasters sip a few drops of wine to make a decision with respect to all the wine waiting to be released for sale.

Unemployment rate in December 2002.

Consumer price index in December 2002.

Ka-fu Wong © 2003

Types of Variables

Whether you have tasted Vanilla Coca Cola (Yes/No).

For a Qualitative or Attribute variable the characteristic being studied is nonnumeric in nature.

Sometimes we will convert qualitative variables to numbers for convenience of calculating summary statistics.

For example, Yes may be coded 1, No may be coded 0. But the coding does not change the nature of the variable.

Ka-fu Wong © 2003

Types of Variables

Minutes remaining in class.

Heights.

Income.

Age.

In a Quantitative variable information is reported numerically.

Ka-fu Wong © 2003

Types of Variables

Quantitative variables can be classified as either discrete or continuous.

Discrete variables: can only assume certain values and there are usually “gaps” between values.

The number of bedrooms in a house.

The number of car accidents per year (1,2,3,…,etc).

The number of students in a class.

The number of ten-cents coin in your pocket today.

The number of sexual partners you have in the past 12 months.

Ka-fu Wong © 2003

Types of Variables

Quantitative variables can be classified as either discrete or continuous.

A continuous variable can assume any value within a specified range.

The pressure in a tire.

The weight of a pork chop.

The height of students in a class.

The amount of water (litre) you drink today.

Time spent on commuting between school and home.

Ka-fu Wong © 2003

Data

Ka-fu Wong © 2003

Levels of Measurement

Nominal,

Ordinal,

Ka-fu Wong © 2003

Levels of Measurement

Nominal level: Data that is classified into categories and cannot be arranged in any particular order.

Eye color.

Place of birth.

Secondary school attended.

Ka-fu Wong © 2003

Levels of Measurement

Ordinal level: involves data arranged in some order, but the differences between data values cannot be determined or are meaningless.

During a taste test of 4 soft drinks, Mellow Yellow was ranked number 1, Sprite number 2, Seven-up number 3, and Orange Crush number 4.

The ranking of MBA programs around the world.

In rating the examples and illustrations given in class

“Very helpful”

Ka-fu Wong © 2003

Levels of Measurement

Interval level: similar to the ordinal level, with the additional property that meaningful amounts of differences between data values can be determined. There is no natural zero point.

Temperature on the Fahrenheit scale.

Wealth (may be negative and positive).

Profit of a company.

Ka-fu Wong © 2003

Levels of Measurement

Ratio level: the interval level with an inherent zero starting point. Differences and ratios are meaningful for this level of measurement.

Hours spent on studying per week.

Weight in kilograms.

Height in centimeters.

Age.

Ka-fu Wong © 2003

Levels of Measurement

Mutually exclusive: An individual, object, or measurement is included in only one category.

“Male” and “female” are two categories that are mutually exclusive.

Heights of “100 up to 150” and “150 up to 200” are two classes that are mutually exclusive.

The marital status (single, married, divorced, separated, windowed) are mutually exclusive.

Heights of “100 up to 150” and “140 up to 200” are two classes that are not mutually exclusive.

Ka-fu Wong © 2003

Levels of Measurement

Exhaustive: Each individual, object, or measurement must appear in one of the categories.

The two categories “Male” and “female” exhaust all possibility of gender.

The two categories “Christian” and “Muslim” do not exhaust all possibilities of religion.

The employment status (employed, unemployment, not in labor force) exhaust all possibilities.

Ka-fu Wong © 2003

Distinguish between a qualitative variable and a quantitative variable.

Distinguish between a discrete variable and a continuous variable.

Distinguish among the nominal, ordinal, interval, and ratio levels of measurement.

Define the terms mutually exclusive and exhaustive.

Chapter One

collecting,

organizing,

presenting,

Ka-fu Wong © 2003

Who Uses Statistics?

Economists,

marketing,

accounting,

We need to provide forecast of the economy (GDP growth).

We need quantitative estimates of

How individual decisions are influenced by policy variables (such as unemployment benefits, education subsidy) in order to forecast the impact of public policies.

How macro policies (government expenditure) will affect output.

Ka-fu Wong © 2003

Who Uses Statistics?

In the business community,

managers must make decisions based on what will happen to such things as

demand,

profits.

These decisions are an effort to shape the future of the organization.

If the managers make no effort to look at the past and extrapolate into the future, the likelihood of achieving success is slim.

Ka-fu Wong © 2003

Why do we need to understand Statistics?

We are constantly deluged with statistics in the media (newspapers, magazines, journals, text books, etc.).

We need to have a means to condense large quantities of information into a few facts or figures.

We need to predict what will likely occur given what has occurred in the past.

We need to generalize what we have learned in specific situations to the more general case.

Ka-fu Wong © 2003

We do not want to become professors of statistics.

We do not want to develop advanced statistics theory.

We are users of statistics

To be effective users, we need to have a good grip of basic statistics theory.

We need to practice using the tools.

This course will give you the basic, enough for you to move on to your next Econometrics class.

Ka-fu Wong © 2003

Types of Statistics

Examples:

A Gallup poll found that 49% of the people in a survey knew the name of the first book of the Bible. The statistic 49 describes the number out of every 100 persons who knew the answer.

According to Consumer Reports, General Electric washing machine owners reported 9 problems per 100 machines during 2001. The statistic 9 describes the number of problems out of every 100 machines.

In our class, xx are female out of yy.

On 2 January, xx% of stocks listed in HK closed lower than the previous trading day.

Ka-fu Wong © 2003

Types of Statistics

Inferential Statistics: A decision, estimate, prediction, or generalization about a population, based on a sample.

A population is a collection of all possible individuals, objects, or measurements of interest.

A sample is a portion, or part, of the population of interest.

Ka-fu Wong © 2003

Types of Statistics

(examples of inferential statistics)

TV networks constantly monitor the popularity of their programs by hiring Nielsen and other organizations to sample the preferences of TV viewers.

The accounting department of a large firm will select a sample of the invoices to check for accuracy for all the invoices of the company.

Wine tasters sip a few drops of wine to make a decision with respect to all the wine waiting to be released for sale.

Unemployment rate in December 2002.

Consumer price index in December 2002.

Ka-fu Wong © 2003

Types of Variables

Whether you have tasted Vanilla Coca Cola (Yes/No).

For a Qualitative or Attribute variable the characteristic being studied is nonnumeric in nature.

Sometimes we will convert qualitative variables to numbers for convenience of calculating summary statistics.

For example, Yes may be coded 1, No may be coded 0. But the coding does not change the nature of the variable.

Ka-fu Wong © 2003

Types of Variables

Minutes remaining in class.

Heights.

Income.

Age.

In a Quantitative variable information is reported numerically.

Ka-fu Wong © 2003

Types of Variables

Quantitative variables can be classified as either discrete or continuous.

Discrete variables: can only assume certain values and there are usually “gaps” between values.

The number of bedrooms in a house.

The number of car accidents per year (1,2,3,…,etc).

The number of students in a class.

The number of ten-cents coin in your pocket today.

The number of sexual partners you have in the past 12 months.

Ka-fu Wong © 2003

Types of Variables

Quantitative variables can be classified as either discrete or continuous.

A continuous variable can assume any value within a specified range.

The pressure in a tire.

The weight of a pork chop.

The height of students in a class.

The amount of water (litre) you drink today.

Time spent on commuting between school and home.

Ka-fu Wong © 2003

Data

Ka-fu Wong © 2003

Levels of Measurement

Nominal,

Ordinal,

Ka-fu Wong © 2003

Levels of Measurement

Nominal level: Data that is classified into categories and cannot be arranged in any particular order.

Eye color.

Place of birth.

Secondary school attended.

Ka-fu Wong © 2003

Levels of Measurement

Ordinal level: involves data arranged in some order, but the differences between data values cannot be determined or are meaningless.

During a taste test of 4 soft drinks, Mellow Yellow was ranked number 1, Sprite number 2, Seven-up number 3, and Orange Crush number 4.

The ranking of MBA programs around the world.

In rating the examples and illustrations given in class

“Very helpful”

Ka-fu Wong © 2003

Levels of Measurement

Interval level: similar to the ordinal level, with the additional property that meaningful amounts of differences between data values can be determined. There is no natural zero point.

Temperature on the Fahrenheit scale.

Wealth (may be negative and positive).

Profit of a company.

Ka-fu Wong © 2003

Levels of Measurement

Ratio level: the interval level with an inherent zero starting point. Differences and ratios are meaningful for this level of measurement.

Hours spent on studying per week.

Weight in kilograms.

Height in centimeters.

Age.

Ka-fu Wong © 2003

Levels of Measurement

Mutually exclusive: An individual, object, or measurement is included in only one category.

“Male” and “female” are two categories that are mutually exclusive.

Heights of “100 up to 150” and “150 up to 200” are two classes that are mutually exclusive.

The marital status (single, married, divorced, separated, windowed) are mutually exclusive.

Heights of “100 up to 150” and “140 up to 200” are two classes that are not mutually exclusive.

Ka-fu Wong © 2003

Levels of Measurement

Exhaustive: Each individual, object, or measurement must appear in one of the categories.

The two categories “Male” and “female” exhaust all possibility of gender.

The two categories “Christian” and “Muslim” do not exhaust all possibilities of religion.

The employment status (employed, unemployment, not in labor force) exhaust all possibilities.

Ka-fu Wong © 2003